Decision tree analysis in determinants of elderly visits in poor rural areas / 北京大学学报(医学版)

ABSTRACT

OBJECTIVE@#To explore the influencing factors of elderly outpatient visits and to provide evidence for poverty reduction in health in the poor rural areas.@*METHODS@#Through stratified sampling, a total of 1 271 aged people in four poverty Qi/County of Ulanqabcity were surveyed, including Qahar Right Wing Front Banner, Qahar Right Wing Middle Banner, Qahar Right Wing Rear Banner and Liangcheng County. Their socioeconomic and demographic characteristics, daily consumption, EuroQol five dimensions questionnaire(EQ-5D) and visual analogue scale (VAS),social support, health service needs and utilization were collected through cross-sectional household questionnaires. 1 039 aged people who had experienced physical discomfort in the past 30 days were selected as subjects for the study. The differences between the groups were analyzed by chi-square test. A Logistic regression equation and a decision tree of elderly visits were built to find factors influencing decisionmaking of the aged.@*RESULTS@#The average age of the research subjects was 71.8 years, with 52.2% being illiterate and 85.8% with middle social support. 58.5% of the subjects living with their spouses, mostly living in 15 min medical circle and without any financial support from their children. The 30-day visiting rate when having physical discomfort was 31.0%. The chi-square test showed that the differences in visit rates among age, ethnic, residence patterns, daily consumption index, housing types, social support scores, grown children's economic assistance, travel time to medical institutions, and health self-assessment scores were statistically significant. Compared with Logistic analysis, the decision tree showed lower error rate of classification. Logistic regression model's error rate of classification was 31.4%, showing that the differences in visit rates among age, ethnic, residence patterns, daily consumption index, social support scores, travel time to medical institutions, and health self-assessment scores were statistically significant. The decision tree model's error rate of classification was 28.6%, showing six main influencing factors, including the travel time to medical institutions, cohabitants, education level, age, whether adult children provide economic support and social support score. The importance of these predictors were 0.42, 0.21, 0.13, 0.11, 0.07 and 0.06, respectively.@*CONCLUSION@#In poor rural areas, medical resources, economic affordability, family and individual socio-demographic characteristics are the key factors affecting decision-making for the aged. It is necessary to integrate the improvement of the health care of the aged into the overall development of the society. And comprehensive interventions should be adopted to improve the outpatient utilization for aged in poor rural areas.